Semiconductor manufacturing involves highly complex techniques for fabricating integrating circuits using semiconductor materials which are layered and patterned onto a substrate, such as silicon.
Photolithography is one technique that may be used to selectively process certain portions of the wafer, e.g., patterning a substrate with lines, e.g., electronic structures. For example, conventional mechanical or optical subsystems of an imaging tool aligner used during fabrication of integrated circuits, is implemented for projecting a mask pattern onto a wafer, e.g., prior to an exposure step. In a conventional lithographic system there is included a projection aligner tool that de-magnifies a pattern on a reticle (mask) and projects it onto a photo resist (photosensitive material) formed on a wafer, and has a light source, an illumination optical system from light source to reticle, and a projection optical system from reticle to a wafer.
As IC device fabrication involves many layers, it is important to ensure that the overlay, or placement of a layer relative to another layer, falls within a certain acceptable tolerance. As such, many parameters of the IC devices, for example the forming of a pattern on a region of a substrate, are monitored during fabrication to ensure that the specifications for performance and reliability may be met.
Further, as the wafer becomes larger and the design rules become tighter, it becomes more important to provide robust variation correction models that provide for real-time process parameter corrections for minimizing observed (measured) variations, e.g., non-linear spatial patterning variations, by adjusting process parameters controlled by the tools used in the lithographic patterning overlay process.
Prior approaches to nonlinear treatment of patterning spatial variation do not account for coupling that takes place between error terms. Thus, current variation correction models are not directly applicable to non-linear diagnostic and control operations. That is, in prior art techniques, the coupling among error components intrinsic to current methods of characterizing nonlinear spatial variations of patterning errors precludes robust nonlinear diagnostics and control of patterning capability.
For example, problems with current Non-linear Overlay models include: the inability to adequately represent observed variation; the exhibition of coupling among terms (non-orthogonality); the limited adaptability/extendibility; the use of poorly behaved functions; the proliferation of non-physical terms, and, the inconsistent use/results across setup/control/analysis/reporting platforms (overlay models are utilized in the lithography process control systems of semiconductor manufacturers, like IBM, and in the products of various lithography and overlay metrology equipment suppliers; notably, ASML, Nikon, KLA-Tencor and Nanometrics).
Current variation models utilize an expansion by solving equations with non-linear terms in an attempt to characterize non-linear distribution over a domain (e.g., a wafer, field, etc.) by coefficients of the expansion. For example, the current methods, practiced by both semiconductor and equipment manufacturers, implement control to minimize variation at sampled locations, i.e., fit measured error to polynomials. However, in polynomials, e.g., power series expansions, used in the current representation of the non-linearity, as currently characterized, even order terms (1, x2, x4, etc.) are coupled, and similarly, odd order terms, (x, x3, x5, etc.) are coupled. The coupled terms offset one another, resulting in unstable coefficients; the degree of instability depends on a variety of factors; including, sampling density, measurement noise, etc. Thus, current methods preclude the assignment of physical meaning to individual coefficients. Moreover, polynomials may not optimally reflect physical variation, particularly in the vicinity of domain boundaries where high order polynomial terms are rapidly varying.
As a consequence of the coupling-driven coefficient instability described above, current methods are restricted to determining coefficients corresponding to the allowed adjustments in a single control loop. This approach is not well suited to the nonlinear overlay control requirements of lithographic patterning; in which multiple control loops, consisting of overlapping subsets of allowed adjustments, pertain to the hierarchical calibration, baseline and runtime control of a tool/process based on different measurements performed at different times. Current methods do not allow the determination of a set of physically meaningful coefficients independent of the measurement and the correspondence of the coefficients to tool/process adjustment in a given control sequence.
In sum, the coupling among error components intrinsic to current methods of characterizing the nonlinear spatial variation of patterning errors precludes robust nonlinear diagnostics and control of patterning capability.
Generally, it would be highly desirable to provide a system and method that provides accurate real-time control of process parameters that minimize nonlinear process variation in a manufacturing step using a process tool.
It would be further desirable to provide a system and method that provides for accurate real-time control of a process parameter utilized in a semiconductor product manufacturing process based on measured attributes of resulting patterns/structures formed as a result of a manufacturing process.
That is, in a semiconductor product manufacturing facility, it would be highly desirable to reduce a coupling among error components intrinsic to current methods of characterizing the nonlinear spatial variation of patterning errors that preclude robust nonlinear diagnostics and control of patterning capability.